Systems used to perform data storage operations of electronic data are growing in complexity. However, current systems may not be able to accommodate increased data storage demands or efficient and timely restore operations. Often, these systems are required to store large amounts of data (e.g. all of a company's data files) during a time period known as a “storage window.” The storage window defines a duration and actual time period when the system may perform storage operations. For example, a storage window may be for twelve hours, between 6 PM and 6 AM (that is, twelve non-business hours). Often, storage windows are rigid and unable to be modified. Therefore, when data storage systems attempt to store increasing data loads, they may need to do so without increasing the time in which they operate. Additionally, many systems perform daily stores, which may add further reliance on completing storage operations during allotted storage windows.
Moreover, each data storage operation requires multiple resources, such as access to a tape drive, allocation of a stream for that tape drive, an available tape on which to store data, a media agent computer to process and monitor the request, and so forth. Given multiple data storage requests and multiple resources, with each request requiring different resources for different periods of time, optimizing allocation of these resources can be a very complex operation as the number of requests and resources grow. Processor time can grow exponentially as the requests and resources grow.
Multidimensional resource allocation is an inherently complex problem to solve. As noted above, a number of disparate resources need to be available to satisfy a single request, such as available media, a compatible drive from a pool of drives, etc. Also, additional constraints must be satisfied, such as a load factor on a computer writing the data, a number of allowed agents or writers to a target media (e.g., disk or tape), etc.
Rules of resource allocation further complicate the problem. For example, rules may be established regarding failover such that when a given drive fails, the system can substitute in another drive. Likewise, rules may be established for load balancing so as not to overtax a given drive, but to spread the burden over a pool of drives. If a primary resource candidate is not available, then the system may allocate resources from an alternate resource pool, which may or may not be satisfactory. Time delay factors arise when alternatives are considered.
Furthermore, resource requests arrive in a random order; however, each incoming request has either a pre-assigned or dynamically changing priority. Furthermore, resources are freed up in a random order and may be associated with lower priority requests. A multiple set matching algorithm is not possible in such a complex environment.
In order to make a best match, a sorted list of requests is often maintained. This queue of requests is then walked and resources allocated to higher priority requests first before lower priority requests can be honored. The matching process for each request is very time consuming given the number of resources that must be made available for each job.
Prior systems have attempted to ameliorate these problems by reducing the number of variables and thereby reducing the complexity of such optimizations of resource allocations. Other systems have employed dedicated resources, often for higher priority requests. However, when those resources become freed up, they sit idle until other requests dedicated to those resources arrive. Other systems have solved this complexity problem by simply reducing the number of requests and creating smaller units of resources. This fragments a system, and can be inefficient.
Requests in a data management system often ultimately fail. For example, a required resource may be down or in short supply. Unfortunately, the data management system has often committed significant resources in the resource allocation process before the request fails. For example, the data management system may spend precious time gathering other resources or data only to discover that the tape drive to which the data should be copied is not available. This causes the data management system to waste time that reduces the amount of productive work that the system can perform during the storage window.
The foregoing examples of some existing limitations are intended to be illustrative and not exclusive. Other limitations will become apparent to those of skill in the art upon a reading of the Detailed Description below. These and other problems exist with respect to data storage management systems.
In the drawings, the same reference numbers and acronyms identify elements or acts with the same or similar functionality for ease of understanding and convenience. To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the Figure number in which that element is first introduced (e.g., element 420 is first introduced and discussed with respect to FIG. 4).
The headings provided herein are for convenience only and do not necessarily effect the scope or meaning of the claimed invention.